1. Field of the Invention
The present invention relates generally to the field of machine fault diagnostics and, more particularly, to a system and method of determining if a machine is operating in a normal state or an abnormal state.
2. Related Art
Over the past few decades industry has taken a variety of steps to improve productivity and quality. However, little attention has been given to the area of maintenance. Maintenance in a broad definition is concerned with controlling the condition of equipment. Although maintenance exists in virtually every manufacturing company, it is often considered to be a support function of a manufacturing process. Only in recent years has maintenance been recognized as an integral part of the manufacturing process, able to increase productivity and quality.
With the increased use of robots, automation, and more sophisticated machines in manufacturing processes, it might be more appropriate to say that productivity and quality depend on machines rather than the person who operates the machine. Robots, for example, have replaced human operators in tasks, such as assembly, loading and unloading, spot welding, and inspection. Keeping this sophisticated equipment in a satisfactory condition increases both the amount and complexity of maintenance. Hence, more repair time and more highly trained, high-priced maintenance technicians and engineers are needed. This, of course, translates to higher maintenance costs.
When the degree of automation increases, maintenance cost also increases. In many companies, maintenance costs represent one of the larger parts of total operating costs--often more than direct labor cost. Therefore, a maintenance strategy that effectively reduces maintenance cost is important for a modern industry to remain competitive.
The three most common maintenance strategies are breakdown or corrective maintenance (i.e., fix the machine when it fails), preventive or time-based maintenance (i.e., maintain machine based on scheduled time), and predictive or condition-based maintenance (i.e., maintain machine before it fails).
For many years, most manufacturing companies used either breakdown or preventive maintenance. In such a case, the machinery is either allowed to breakdown or routine maintenance is performed to reduce the risk of machine failures. Breakdown maintenance is suitable only when a machine is not important, and is inexpensive to replace. If the cost of lost production, potential secondary damage to machinery, and potential safety risks are high, then this strategy is unacceptable. An apparent improvement to this strategy is to use preventive maintenance.
Although preventive maintenance can reduce the occurrence of machine breakdown, it also has some problems. First, the period between overhauls is very difficult to determine because machines and their components do not fail at regular intervals. Second, precious production time is lost because it is prudent to examine as many components as possible during the overhaul period. Third, parts in reasonable condition are often replaced.
Therefore, the best strategy appears to be to adopt a predictive maintenance strategy which predicts the condition, performance, and reliability of machinery, so that maintenance can be planned in advance. Recently, due to the increasing requirement of product quality and manufacturing automation, more and more manufacturing companies have adopted predictive maintenance as part of their maintenance program. They are doing so in order to increase reliability, productivity, and availability while minimizing costs of maintenance and overall plant operation.
The goal of predictive maintenance is the elimination of machinery breakdowns by applying technologies to measure the condition of machines, identify any present or impending problems, and predict when corrective action should be performed. There are several benefits derived from predictive maintenance [see Pardue, E., et al., "Elements of Reliability-Based Machinery Maintenance," Sound and Vibration, May 1992, pp. 14-20].
First, the condition of machines under a predictive program is known, permitting repairs to be planned and carried out without interrupting production. Thus, maintenance work activities are more efficiently planned from the standpoint of workers, parts, and tools.
Second, product quality is improved. Product quality is often adversely affected by mechanically degraded equipment. Since quality is often measured as a final process step, large amounts of unacceptable quality product may be manufactured before the problem is detected. Predictive technologies can measure the mechanical condition of machinery so that corrections can be made before quality is compromised.
Third, safety is enhanced by eliminating unnecessary preventive maintenance work and eliminating extensive maintenance work resulting from catastrophic failure. Since maintenance activities are anticipated, planned, and carried out in a non-emergency environment, exposure to hazardous conditions is reduced.
Fourth, energy savings can be substantial. Since the elimination of high-energy vibration sources such as misalignment and imbalance can reduce machine power consumption by 10 to 15 percent, predictive maintenance provides several potential areas for energy savings.
Vibration data is the most widely used method for monitoring the condition of a machine due to its sensitivity and ability to provide early prediction of developing defects (see Serridge, M., "Ten Crucial Concepts Behind Trustworthy Fault Detection in machine Condition Monitoring," Proceedings of the 1st International Machinery Monitoring and Diagnostics Conference, Las Vegas, Nevada, 19891, pp. 722-723). Although other process parameters (oil analysis, temperature, pressure, etc.) can be useful in giving early warning of machine breakdowns, they do not give as wide a range of fault types as vibration.
The suitability of vibration based analysis methods for machine health monitoring has been well documented. Vibration is known to provide the best and most comprehensive measure of machine condition compared with other measurement methods (See Angels, M., "Choosing Accelerometers for Machinery Health Monitoring," Sound and Vibration, December 1990, pp. 20-24). Vibration analysis allows the characterization of most of the dynamic solicitations in rotating machines, in particular those generated by abnormal running order. Furthermore, this method of analysis is easy to implement and efficient since machines remain running during the collection of vibration signals.
Vibration is directly correlated to machine longevity in two ways:
(1) A low vibration level when new a machine is new generally indicates that the machine Will last a long time (i.e., at or above its expected life). PA1 (2) The vibration level increases when a machine is heading for a breakdown.
Using vibration data to improve maintenance operations is obtained by eliminating the purchase of unnecessary parts, doubling the life of the machinery, and decreasing energy consumption as a result of reducing the amount of noise and vibration generated.
Initiating a predictive maintenance program has a significant indirect benefit, in addition to those already mentioned. If critical machine components are monitored and replaced immediately when an abnormality occurs, the life of machine tools should increase due to the minimization of stress under high machine vibrations.
The health of bearings are crucial to the operation of machine tools and therefore, most vibration analysis programs have been initiated to monitor hearings. Ninety percent of bearing failures can be predicted months ahead, which provides more than adequate incentive for adopting monitoring and fault detection techniques for bearings.
The primary causes of bearing failures are: contamination, including moisture, overstress, lack of lubrication, and defects created after manufacturing. Bearings typically achieve only about 10 percent of their rated life. Tests of bearing life under laboratory conditions yield lives of 100 to 1000 years. Therefore, having the capability to determine the root cause of bearing failure is vital in preventing a recurrence of the problem and extending the life of the bearing.
Over the last two decades, most of the machine monitoring and diagnostic systems have been performed off-line using signal processing techniques. The success of these systems is not due to any one signal processing technique, but to the large amount of redundancy associated with multiple signal processing.
However, those signal processing techniques are very complicated to use; in addition, they must be performed by a highly trained and experienced human analyzer in order to make an accurate diagnosis. Accurate fault diagnostics is essential, especially in reducing product cycle time. As a result of correct and rapid fault diagnostics, equipment maintainability and availability can be improved significantly, thereby reducing the product cycle time.
Thus, what is needed is an effective machine monitoring technique for early detection of failure in critical machine components in order to prevent shutdowns and maintain production goals with high quality parts.